1. Field of the Invention
The present invention relates generally to diagnosis of sleep-disordered breathing and in particular to detection of sleep-disordered breathing events utilizing electrocardiographic measurements.
2. Description of the Related Art
Sleep-disordered breathing is a term applicable to a wide variety of sleep-related breathing disorders of diverse pathophysiologies that share the common characteristic of recurrent episodes of apnea and hypopnea during sleep. Sleep-disordered breathing has become a significant problem for a large portion of the population. In fact, sleep-disordered breathing has become prevalent in about 5% of the adult population and in almost 50% of patients with congestive heart failure.
Among the most common types of sleep-disordered breathing is sleep apnea, in which patients experience a complete cessation of air flowing into the lungs for periods exceeding ten seconds. Partial or complete arousal from sleep is considered a defensive mechanism most likely stimulated by rising carbon dioxide levels in the blood during the apneic event to reestablish ventilation and prevent death in the sleeping subject. There are three recognized types of sleep apnea: Central sleep apnea, Obstructive sleep apnea, and Cheyne-Stokes breathing. Central sleep apnea events are characterized by the suspension of all respiratory movement due to a decreased neural input from the brain to the muscles of inspiration. Obstructive sleep apnea is characterized by an upper airway occlusion. Cheyne-Stokes breathing is a waxing-waning pattern of breathing seen commonly in heart failure patients. The other form of sleep-disordered breathing, hypopnea, takes the form of a decrease in ventilation during sleep rather than a complete sleep apnea and is characterized by a reduction in the amplitude of breathing resulting in oxygen desaturation.
While the distinction between apnea and hypopnea is largely one of severity, sleep-disordered breathing diagnosis may entail measurement of both types of events. The Apnea-Hypopnea Index, representing a number of either apneic or hypopneic events per hour for a subject, is more commonly used than the Apnea Index, representing only the total number of apneic events per hour for the subject. An Apnea-Hypopnea Index of more than 5 events per hour, regardless of severity, is usually qualified as sleep apnea. Other variables such as average duration of an event, number of apneic versus hypopneic events, and average decrease in blood oxygen saturation during events are utilized to determine the severity of the disorder.
Polygraphic monitoring, or nocturnal polysomnography (the measurement of vital body signals during sleep) is the most commonly employed method of diagnosing sleep disorders, including sleep apnea. The overnight sleep study typically includes measurement of oronasal flow, thoracic and abdominal respiratory efforts, electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram (EOG), chin and leg electromyogram (EMG), snoring sounds and pulse oximetry. The various signals are recorded during the night to identify different sleep stages, respiratory variables, heart function, and muscle tone, all of which aid in scoring sleep-disordered breathing events. The data from the measurements is collected during the patient's normal sleeping time and is later scored by a sleep specialist who visually examines the polygraph recording, identifying sleep stages and sleep-disordered breathing events causing oxygen desaturation. The sleep specialist's report contains a measure of sleep-disordered breathing known as the Apnea-Hypopnea Index (AHI), which refers to the number of irregular breathing events per hour of sleep.
Conventional polygraphic monitoring instrumentation is often very uncomfortable to the patient. For example, direct methods of respiration monitoring such as use of nasal thermistors, spirometers, and pneumotachometers, which measure air flow in and out of the lungs, generally interfere with normal respiration, and indirect methods, such as whole body plethysmographs, inductance and impedance plethysmographs, and strain gauge measurement of chest and abdomen circumference, which measure the effects of respiration on the body, either lose their calibration readily or immobilize the patient.
Polygraphic monitoring can have other significant disadvantages. For example, the cost of implementing polygraphic monitoring can exceed $1,500 per night making long-term studies cost prohibitive. Also, the methods of assessing long-term prognosis of sleep-disordered breathing require extensive patient cooperation. Further, sleep laboratories are inaccessible to a large part of the population due to limited facilities and long waiting lists. Home polysomnography, which allows the patient to conduct the test at home unattended, offers the patient a less expensive alternative, however, it has its own disadvantages including reduced accuracy.
Thus, there is a need for a system, software, and related methods that can detect events of sleep-disordered breathing that is simple to use, relatively inexpensive, noninvasive, and that requires only minimal patient cooperation.
Sleep-disordered breathing is prevalent in individuals suffering from cardiovascular disease. ECG signals are routinely recorded in studies for patients with cardiac problems, as well as in patients having respiratory disorders, sleep disorders, and patients in intensive care units. Millions of patients are screened each year using extended ECG monitoring (at least 24 hours), while generally their respiration is not monitored due to the added cost and inconvenience of conventional airflow monitoring equipment. Established technology has existed for years for measurement of the ECG in patients and advances in the field of electrocardiography have rendered analysis and conditioning of ECG signals robust. Measurement of ECG signals does not interfere with normal breathing and is more comfortable and less intrusive for the patient than polygraphic monitoring. Also, properly attached ECG leads are less prone to error due to patient movement. Correspondingly, use of the ECG to detect sleep-disordered breathing as an alternative to nocturnal polysomnography has been receiving increasing attention. Further, the ECG can provide cardiologists with simultaneous sleep-disordered breathing data and cardiac muscle activity data that may help improve diagnosis and treatment of associated cardiac disorders.
Investigators have examined various methodologies involving utilizing one or more parameters derived from the ECG to discriminate between normal breathing and breathing associated with sleep-disordered breathing. Generally, the focus is on deriving a waveform similar to respiration from the ECG. One methodology known as Heart Rate Variability identifies variations in the power spectrum of a time series of instantaneous heart rate calculated from the ECG. Another methodology known as Angle of Mean Electrical Axis also synonymously referred to as ECG-Derived Respiration (EDR) utilizes two orthogonal leads of the ECG to estimate an angle of the electrical axis. When used as the sole means of detecting sleep-disordered breathing, however, the detection results have been less than stellar. Proposed attempts to improve the reliability of this methodology in obtaining EDR include using 8 leads; however, no application of this multiple-lead methodology has been proposed for detecting sleep-disordered breathing.
More recently, an investigator has proposed a methodology deriving EDR from a single lead ECG combined with other parameters such as Heart Rate Variability to detect sleep apnea. EDR is first derived. The methodology then takes measurements using the power spectrum density of a timeseries of instantaneous heart rate, the power spectrum density of the EDR utilizing a sequence of R-wave areas, and a discrete sequence for one-minute time intervals, takes time domain ECG measurements from R-R intervals, and combines these measures to produce a diagnostic measure. Though proposed to use as little as one lead of the ECG, the methodology still requires use of instantaneous heart rate for the detection of sleep-disordered breathing.
Recognized by Applicant is that the morphology of the R-wave peak amplitudes on the QRS complex during obstructive sleep apnea episodes exhibited a variation which can negate the need for instantaneous heart rate for the detection of sleep-disordered breathing. Thus, it would be desirable to provide a system, software, and related methods to quantify the morphology of the QRS complex as a methodology of detecting sleep-disordered breathing, using either the morphology of the amplitude of the R-wave peaks in the ECG signal to detect the presence of sleep apnea or the morphology of the area under the QRS complex of the ECG signal, without requiring the use of instantaneous heart rate. It would also be desirable to provide such a system, software, and related methods that can detect events of sleep-disordered breathing utilizing ECG signal measurements, alone, that have high sensitivity and specificity.